When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Lamor frequency, the net aligned moment, Mz, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known image reconstruction techniques.
Depending on the technique used, many NMR scans currently used to produce medical images require many minutes to acquire the necessary data. The reduction of this scan time is an important consideration, since reduced scan time increases patient throughput, improves patient comfort, and improves image quality by reducing motion artifacts. Many different strategies have been developed to shorten the scan time.
One such strategy is referred to generally as “parallel imaging”. Parallel imaging techniques use spatial information form arrays of RF receiver coils to substitute for the encoding which would otherwise have to be obtained in a sequential fashion using RF pulses and field gradients (such as phase and frequency encoding). Each of the spatially independent receiver coils of the array carriers certain spatial information and has a different sensitivity profile. This information is utilized in order to achieve a complete location encoding by a combination of the simultaneously acquired coil data from the separate receiver coils. Specifically, parallel imaging techniques undersample k-space by reducing the number of phase-encoded lines acquired by increasing the distance between these lines while keeping the maximal extent covered in k-space fixed. The combination of the separate NMR signals produced by the separate receiver coils enables a reduction of the acquisition time required for an image (in comparison to conventional k-space data acquisition) by a factor which in the most favorable case equals the number of the receiver coils. Thus the use of multiple receiver coils acts to multiply imaging speed, without increasing gradient switching rates or RF power.
Two categories of such parallel imaging techniques which have recently been developed and applied to in vivo imaging are SENSE (SENSitivity Encoding) and SMASH (SiMultaneous Acquisition of Spatial Harmonics).
With SENSE, the undersampled k-space data is first Fourier transformed to produce an aliased image from each coil, and then the aliased image signals are unfolded by a linear transformation of the superimposed pixel values.
With SMASH, the omitted k-space lines are filled in or reconstructed prior to Fourier transformation, by constructing a weighted combination of neighboring lines acquired by the difference receiver coils. SMASH requires that the spatial sensitivity of the coils be determined, and one way to do so is by “autocalibration” which entails the use of variable density k-space sampling. A recent important advance to SMASH techniques using autocalibration is a technique known as GRAPPA (GeneRalized Autocalibrating Partially Parallel Acquisitions), introduced by Griswold et al. This technique is described in U.S. Pat. No. 6,841,998 as well as in the article titled “Generalized Autocalibrating Partially Parallel Acquisitions (GRAPPA),” by Griswold et al. and published in Magnetic Resonance in Medicine 47:1202-1210 (2002).
Using these original GRAPPA techniques, lines near the center of k-space are sampled at the Nyquist frequency (in comparison to the greater spaced lines at the edges of k-space). These so-called autocalibration signal (ACS) lines are then used to determine the weighting factors that are used to reconstruct the missing k-space lines. In particular, a linear combination of individual coil data is used to create the missing lines of k-space. The coefficients for the combination are determined by fitting the acquired data to the more highly sampled data near the center of k-space.
In dynamic MRI applications, such as functional imaging, interventional imaging and cardiac imaging, there has long been a need in the art for methods and apparatus that provide high quality images, i.e., high-resolution and signal to noise ratio (SNR). Thus, in addition to scan time, it is also desirable to improve the SNR of acquired image data.